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Data Analysis Core

数据分析核心

基本信息

批准号：
10701828
负责人：
Jeffrey M Spraggins
金额：
$ 57.51万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10701828
关键词：
3-Dimensional Age Anatomy Atlases Biological Biological Assay Cell Cycle Cells Chemicals Classification Clinical Communities Computer software Cues Data Data Analyses Data Set Data Sources Development Dimensions Dropout Eye Gender Gene Expression Goals Human Human BioMolecular Atlas Program Image Imaging technology Individual Kidney Label Learning Machine Learning Magnetic Resonance Imaging Maps Masks Measurement Medical Imaging Memory Methods Microscopy Mining Mission Modality Modeling Molecular Molecular Analysis Multimodal Imaging Nature Nephrons Organ Pancreas Population Procedures Process Production Proteomics Published Comment Quality Control Reporting Research Resolution Resources Sampling Scheme Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Speed Technology Tissue Donors Tissue Sample Tissues Universities Variant Visualization Work X-Ray Computed Tomography automated segmentation cell type cohort computerized data processing computing resources data integration data mining demographics human tissue imaging facilities insight interest member multimodal data multimodality multiple omics open source spatial integration spatiotemporal tissue mapping transcriptomics

项目摘要

PROJECT SUMMARY – Data Analysis Core. The characterization pipelines built by the VU Biomolecular Multimodal Imaging Center (BIOMIC) Data Analysis Core (DAC) will produce a multimodal molecular atlas of kidney across multiple scales in 2-D and 3-D, comprising rich and varied molecular data from MALDI IMS, MxIF, CODEX, spatial and non-spatial proteomics/ transcriptomics, united by common spatial coordinate space. The DAC will build on our previous developments, including spatial segmentation for both acquisition and analysis, comprehensive molecular identification across modalities, automated data mining across modalities by spatial masks using clinical and temporal cues, and situating data within an “average” human kidney developed from 1000s of medical images. In Aim 1 we will further develop and scale our modality-specific processing methods to prepare the different measurement types for subsequent spatial integration, multimodal analysis, and content/cross-omic relationship mining. Aim 2 will focus on expanding and scaling our characterization pipeline, bringing together the broad array of distinct imaging and -omics measurement types individually processed by the methods in Aim 1. By integrating these varied datasets spatially, temporally, as well as content-wise, we will empirically mine them for cross-modal and molecular-functional relationships. These technologies will establish both 2-D and 3-D multimodal tissue maps that concurrently report hundreds to thousands of biomolecules at cellular resolutions, together with corresponding functional and cell type annotations. In Aim 3 we will compose all spatial and molecular information into a reference atlas by expanding the spatio-temporal mapping of molecular variation, organization, and function beyond single tissue block analyses (as in Aim 2) to multi-block and multi-donor tissue cohorts. Finally, Aim 4 will enable continued coordination with the HuBMAP consortium and HIVE team members by integrating our analyses into the HIVE’s ASCT+B tables for interactive exploration, visualization, and searchable analyses.

项目总结-数据分析核心。